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Differential evolution for dynamic environments with unknown numbers of optima

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  • Mathys Plessis
  • Andries Engelbrecht

Abstract

This paper investigates optimization in dynamic environments where the numbers of optima are unknown or fluctuating. The authors present a novel algorithm, Dynamic Population Differential Evolution (DynPopDE), which is specifically designed for these problems. DynPopDE is a Differential Evolution based multi-population algorithm that dynamically spawns and removes populations as required. The new algorithm is evaluated on an extension of the Moving Peaks Benchmark. Comparisons with other state-of-the-art algorithms indicate that DynPopDE is an effective approach to use when the number of optima in a dynamic problem space is unknown or changing over time. Copyright Springer Science+Business Media, LLC. 2013

Suggested Citation

  • Mathys Plessis & Andries Engelbrecht, 2013. "Differential evolution for dynamic environments with unknown numbers of optima," Journal of Global Optimization, Springer, vol. 55(1), pages 73-99, January.
    • Handle: RePEc:spr:jglopt:v:55:y:2013:i:1:p:73-99
    DOI: 10.1007/s10898-012-9864-9
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    References listed on IDEAS

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    1. Massimo Spadoni & Luciano Stefanini, 2012. "A Differential Evolution algorithm to deal with box, linear and quadratic-convex constraints for boundary optimization," Journal of Global Optimization, Springer, vol. 52(1), pages 171-192, January.
    2. Weihang Zhu, 2011. "Massively parallel differential evolution—pattern search optimization with graphics hardware acceleration: an investigation on bound constrained optimization problems," Journal of Global Optimization, Springer, vol. 50(3), pages 417-437, July.
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